Malaria is a major cause of mortality and morbidity in Sub-Saharan Africa (SSA) and one of the biggest impediments to the economic development of the region. Malaria is transmitted by Anopheles mosquitoes and one of the major methods for controlling these mosquitoes is through the use of chemical insecticides. Unfortunately, there are only four chemical classes of insecticides licensed for used for mosquito control, and resistance has already emerged to all classes although with a marked difference in levels. This resistance is a major threat to the recent advances the malaria control community has made in reducing both deaths and malaria infections. To reduce the impact of resistance, malaria control program managers need to know when resistance is emerging in their intervention areas and put in place measures to prevent its spread e.g. switching to a new insecticide. The most effective way of doing this is through the use of DNA markers which can detect the presence of a resistance associated marker in a mosquito population before it reaches high frequency. It is the primary goal of this proposal to revolutionize the application of molecular markers for insecticide resistance across the malaria endemic countries of SSA by providing control programs with new markers and geographically-calibrated maps that predict their impact on resistance phenotypes. The unifying theme of this project is to exploit the power of whole genome sequencing to identify genes/regulatory regions that are associated with insecticide resistance. We will use a combination of approaches including computer-based searches of existing genome data bases; large scale collections and resistance screening of malaria mosquitoes in both East and West Africa, and laboratory crossing experiments to identify rare, resistance-associated variants. All these studies will be underpinned by extensive whole genome sequencing performed by the Wellcome Trust Sanger Institute. A major secondary outcome of this project will be the release of these genome data sets to the research community which will be a powerful resource for vector biologists. Once we have identified resistance-associated DNA markers we will calibrate their links with resistance and then map their frequencies and distributions in expanded collections of mosquitoes provided by a network of collaborators in SSA. We will use modelling approaches to produce marker maps from these data, including the sensitivity of their predictive power to key environmental variables. Our project will develop and apply new bioinformatic, laboratory and field methodologies to identify DNA markers and associated mapping resources for current and future insecticides, providing long-term insecticide resistance management tools for control programs.